Video sources and inertial sensors are attached to a weapon and to goggles. A computer receives video images from the weapon- and goggles-mounted sources and inertial data from the sensors. The computer calculates a location for an image from the weapon-mounted source within an image from the goggles-mounted source using the inertial sensor data. The sensor-based location is checked (and possibly adjusted) based on a comparison of the images. A database contains information about real-world objects in a field of view of the goggles-mounted source, and is used to generate icons or other graphics concerning such objects.
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2. The method of claim 1, wherein the first motion data and the third motion data comprise data from an inertial measurement unit (IMU) sensor coupled to the first video source, and wherein the second motion data and the fourth motion data comprise data from an IMU sensor coupled to the second video source.
This invention relates to video stabilization systems that use motion data from inertial measurement units (IMUs) to correct unwanted motion in video footage. The problem addressed is the presence of shaky or unstable video caused by unintended movements of the camera during recording. Traditional stabilization methods often rely solely on image processing, which can be computationally intensive and may not fully compensate for rapid or complex motions. The invention involves a system with two video sources, each equipped with an IMU sensor. The first video source captures video while its IMU sensor records motion data, and the second video source does the same. The system processes the motion data from both IMUs to analyze and compensate for unwanted motion in the video streams. By comparing the motion data from both sensors, the system can distinguish between intentional camera movements and unintended shakes, allowing for more accurate stabilization. The motion data from the IMUs is used to adjust the video frames in real-time or during post-processing to produce smoother, stabilized video output. This approach improves upon traditional methods by incorporating direct motion measurements from the IMUs, reducing reliance on image-based stabilization alone. The system is particularly useful in applications where cameras are subjected to significant motion, such as action sports, aerial filming, or handheld recording.
3. The method of claim 1, wherein the motion data adjustment comprises an amount of rotation.
This invention relates to motion data adjustment in systems that track or analyze movement, such as in robotics, virtual reality, or motion capture. The problem addressed is ensuring accurate motion data by compensating for rotational discrepancies that may arise during tracking or processing. The method involves adjusting motion data by applying a rotational correction to account for deviations in orientation. This adjustment ensures that the motion data accurately reflects the intended or actual movement, improving system performance in applications requiring precise motion tracking. The rotational adjustment may be applied to correct for sensor misalignment, environmental interference, or other factors causing rotational errors. The method may be used in conjunction with other motion data adjustments, such as translational corrections, to provide comprehensive motion compensation. The invention is particularly useful in systems where small rotational errors can significantly impact performance, such as in robotic navigation, augmented reality, or biomechanical analysis. By incorporating rotational adjustments, the system achieves higher accuracy and reliability in motion tracking and analysis.
6. The method of claim 5, wherein the graphic indicia comprise icons scaled based on distances between the first video source and the identified objects.
This invention relates to a method for displaying graphic indicia in a video stream, addressing the challenge of visually representing spatial relationships between a video source and objects within the scene. The method involves identifying objects in a video stream captured by a first video source, such as a camera, and determining their distances from the video source. Graphic indicia, such as icons, are then generated and overlaid on the video stream. The size of these icons is dynamically scaled based on the calculated distances between the video source and the identified objects. Closer objects are represented with larger icons, while farther objects are shown with smaller icons, providing an intuitive visual cue about their relative positions. The method may also involve tracking the movement of objects and adjusting the graphic indicia accordingly to maintain accurate spatial representation. This approach enhances situational awareness by clearly indicating the proximity of objects to the video source, which is particularly useful in applications like surveillance, navigation, or augmented reality. The invention ensures that the graphic indicia remain proportionally accurate to the real-world distances, improving the user's ability to interpret the scene.
7. The method of claim 1, further comprising wirelessly receiving data from the second video source.
This invention relates to video processing systems that integrate multiple video sources, particularly for applications like surveillance, broadcasting, or live event production. The problem addressed is the need to efficiently combine video feeds from different sources while maintaining synchronization and minimizing latency. The invention describes a method for processing video data from at least two video sources, where the first video source provides a primary video feed, and the second video source provides an auxiliary video feed. The method includes capturing video data from the first video source, processing the video data to generate a primary video output, and then wirelessly receiving data from the second video source. The auxiliary video data from the second source is synchronized with the primary video feed to ensure seamless integration. The system may also include features for dynamically adjusting the video processing parameters based on the content of the auxiliary feed, such as switching between sources or overlaying the auxiliary feed onto the primary feed. The wireless reception of data from the second source allows for flexible deployment of cameras or other video capture devices without the need for physical connections, reducing setup complexity and improving scalability. The invention aims to provide a robust solution for real-time video integration in environments where multiple video inputs must be managed efficiently.
8. The method of claim 1, wherein the first video source is mounted to a helmet and the second video source is mounted to a weapon.
A system captures and processes video from multiple sources to enhance situational awareness for users, such as military personnel or law enforcement. The primary challenge is integrating video feeds from different perspectives to provide a comprehensive view of the environment. The invention addresses this by synchronizing and combining video streams from at least two sources. One video source is mounted to a helmet, providing a first-person perspective aligned with the user's head movements. The second video source is mounted to a weapon, offering a targeted view of the user's aim. The system processes these feeds to align, stabilize, and overlay them, ensuring accurate spatial and temporal correlation. This integration allows the user to monitor both their immediate surroundings and the weapon's field of view simultaneously, improving reaction time and decision-making. The system may also include additional features such as image stabilization, real-time annotations, or augmented reality overlays to further enhance usability. The combined video output can be displayed on a heads-up display or other output device, providing a unified view of the environment. This approach is particularly useful in high-stress scenarios where rapid situational assessment is critical.
10. The system of claim 9, wherein the first motion data and the third motion data comprise data from an inertial measurement unit (IMU) sensor coupled to the first video source, and wherein the second motion data and the fourth motion data comprise data from an IMU sensor coupled to the second video source.
This invention relates to a system for capturing and processing video data from multiple video sources, particularly for applications requiring synchronized or coordinated video capture. The system addresses the challenge of accurately aligning video streams from different sources, which is critical in applications such as virtual reality, augmented reality, surveillance, or multi-camera setups where motion synchronization is essential. The system includes at least two video sources, each equipped with an inertial measurement unit (IMU) sensor. The IMU sensors provide motion data that captures the movement and orientation of each video source. The first video source generates first motion data and first video data, while the second video source generates second motion data and second video data. Additionally, the system may include a third video source generating third motion data and third video data, and a fourth video source generating fourth motion data and fourth video data. The motion data from the IMU sensors is used to synchronize or align the video streams, ensuring that the captured video data is temporally and spatially consistent. This synchronization is particularly useful in scenarios where multiple cameras are moving independently, such as in handheld or wearable camera systems, or in applications requiring precise motion tracking. The system may further process the synchronized video data for applications such as 3D reconstruction, motion analysis, or multi-view rendering.
11. The system of claim 9, wherein the motion data adjustment comprises an amount of rotation.
A system for adjusting motion data in a tracking or navigation application, particularly for devices like drones, robots, or wearable sensors, addresses inaccuracies in motion tracking caused by environmental interference or sensor noise. The system processes raw motion data from one or more sensors, such as accelerometers, gyroscopes, or magnetometers, to correct positional and orientation errors. The adjustment includes modifying the motion data by applying a rotational transformation to align the tracked motion with a reference frame or desired trajectory. This rotational adjustment compensates for drift or misalignment in the sensor readings, improving the accuracy of the system's positional and orientation outputs. The system may integrate the adjusted motion data over time to estimate a corrected path or orientation, ensuring reliable navigation or tracking performance in dynamic environments. The rotational adjustment can be dynamically calculated based on real-time sensor inputs or predefined calibration parameters, allowing for adaptive correction in varying conditions. This approach enhances the precision of motion tracking applications where accurate positional and orientation data are critical.
14. The system of claim 13, wherein the graphic indicia comprise icons scaled based on distances between the first video source and the identified objects.
The system relates to a visual display technology that enhances situational awareness by dynamically adjusting graphic indicia, such as icons, to reflect spatial relationships between a video source and identified objects. The invention addresses the challenge of presenting clear and intuitive visual information in environments where objects are at varying distances from the observer, such as in surveillance, navigation, or augmented reality applications. The system processes video input from a first video source to detect and identify objects within the field of view. Once identified, these objects are annotated with graphic indicia, such as icons, symbols, or labels, to provide additional context or alerts. The key innovation is the scaling of these graphic indicia based on the measured distances between the video source and the identified objects. Closer objects are represented with larger or more prominent indicia, while more distant objects are scaled down proportionally. This dynamic scaling ensures that the visual annotations remain legible and proportionally accurate, improving user comprehension of spatial relationships. The system may also incorporate additional features, such as adjusting the opacity or color of the indicia based on distance or other contextual factors, to further enhance clarity. The technology is particularly useful in applications requiring real-time spatial awareness, such as autonomous vehicles, drone navigation, or military reconnaissance.
16. The system of claim 9, wherein the first video source is mounted to a helmet and the second video source is mounted to a weapon.
A system captures and processes video from multiple sources to enhance situational awareness for users, particularly in military or tactical environments. The system includes at least two video sources: one mounted to a helmet and another mounted to a weapon. The helmet-mounted video source provides a first-person perspective, capturing the user's field of view, while the weapon-mounted video source captures a forward-looking view aligned with the weapon's direction. The system synchronizes and processes the video streams to generate a composite output, which may include overlaying the weapon-mounted video onto the helmet-mounted video or vice versa. This allows the user to see both their own perspective and the weapon's perspective simultaneously, improving target acquisition and situational awareness. The system may also include stabilization, image enhancement, and real-time processing to ensure clear and accurate visual information. The synchronized video streams can be displayed on a heads-up display (HUD) or other output device, providing the user with a unified view of their environment. This technology addresses the need for improved visual feedback in dynamic and high-stress scenarios, such as combat or law enforcement operations.
18. The method of claim 1, wherein the data associated with the portion of the first image is based on pixels for a first region of the first image, and wherein the data associated with the portion of the second image is based on pixels for a second region of the second image.
This invention relates to image processing, specifically methods for analyzing and comparing regions within multiple images. The problem addressed involves accurately extracting and correlating data from specific portions of different images to improve analysis, such as in object detection, image alignment, or feature matching. The method processes a first image and a second image, focusing on distinct regions within each. For the first image, data is derived from pixels within a first region, while for the second image, data is derived from pixels within a second region. These regions may correspond to areas of interest, such as objects, features, or patterns, and the extracted data can include pixel values, statistical measures, or other image characteristics. The method ensures that the data from each region is independently processed, allowing for precise comparison or further analysis between the two images. This approach enhances accuracy in tasks like image registration, change detection, or multi-image feature extraction by isolating relevant portions of each image for targeted analysis. The technique is particularly useful in applications requiring high precision, such as medical imaging, surveillance, or automated quality inspection.
19. The system of claim 9, wherein the data associated with the portion of the first image is based on pixels for a first region of the first image, and wherein the data associated with the portion of the second image is based on pixels for a second region of the second image.
This invention relates to image processing systems that analyze and compare regions of multiple images. The system addresses the challenge of accurately correlating and processing specific portions of images, particularly when those portions are derived from different regions of separate images. The system includes a processing unit that extracts data from a first region of a first image and a second region of a second image. The extracted data is based on pixel values within these regions, allowing for precise comparison or analysis of the selected portions. The system may further include a display unit to present the processed data, enabling visualization of the results. The invention is particularly useful in applications requiring detailed image analysis, such as medical imaging, surveillance, or quality control, where accurate region-based comparisons are essential. By focusing on specific regions rather than entire images, the system improves efficiency and accuracy in identifying and processing relevant image data.
20. The non-transitory machine-readable medium of claim 17, wherein the data associated with the portion of the first image is based on pixels for a first region of the first image, and wherein the data associated with the portion of the second image is based on pixels for a second region of the second image.
This invention relates to image processing, specifically techniques for analyzing and comparing regions of multiple images. The problem addressed involves accurately correlating and processing specific portions of images to extract meaningful data, particularly when dealing with misaligned or differently framed images. The solution involves a non-transitory machine-readable medium storing instructions that, when executed, perform operations to process image data. The instructions include obtaining a first image and a second image, where each image contains a portion of interest. The data associated with the portion of the first image is derived from pixels within a first region of the first image, while the data associated with the portion of the second image is derived from pixels within a second region of the second image. The regions may correspond to overlapping or aligned areas between the images, or they may represent distinct sections for comparative analysis. The system ensures that the data extracted from each image is based on the relevant pixels within these defined regions, enabling precise comparison or further processing. This approach is useful in applications such as object tracking, image registration, or change detection, where accurate alignment and region-based analysis are critical. The method improves upon prior techniques by explicitly defining the pixel-based regions for data extraction, reducing errors caused by misalignment or irrelevant pixel data.
22. The method of claim 21, wherein the first motion data and the third motion data comprise data from an inertial measurement unit (IMU) sensor coupled to the first video source, and wherein the second motion data and the fourth motion data comprise data from an IMU sensor coupled to the second video source.
This invention relates to a system for capturing and processing video data from multiple video sources, particularly focusing on synchronizing video streams using motion data from inertial measurement units (IMUs). The problem addressed is the difficulty in accurately aligning video streams from different sources, which is critical for applications like 360-degree video capture, virtual reality, and multi-camera surveillance. Traditional synchronization methods often rely on time stamps or external reference signals, which can be unreliable or require additional hardware. The invention involves a method where motion data from IMU sensors attached to each video source is used to synchronize the video streams. Specifically, the first video source generates first motion data and third motion data, while the second video source generates second motion data and fourth motion data. The IMU sensors provide motion information, such as acceleration and angular velocity, which is used to align the video streams temporally. This approach ensures that the video data from different sources is synchronized based on real-time motion tracking, improving accuracy and reducing the need for external synchronization signals. The method may also involve processing the motion data to compensate for discrepancies between the video sources, such as differences in frame rates or sensor noise. By leveraging IMU data, the system achieves precise synchronization without relying solely on time stamps or external references, enhancing the reliability of multi-source video capture systems.
23. The method of claim 21, wherein the motion data adjustment comprises an amount of rotation.
A system and method for adjusting motion data in a tracking or navigation application involves processing sensor inputs to correct positional or orientation errors. The method includes receiving motion data from one or more sensors, such as accelerometers, gyroscopes, or magnetometers, and applying adjustments to this data to improve accuracy. These adjustments may involve compensating for drift, noise, or environmental interference. In particular, the adjustment process includes modifying the motion data by a specified amount of rotation to align the tracked position or orientation with a reference frame or desired trajectory. The rotation adjustment may be applied in one or more axes to correct deviations detected during motion tracking. This technique is useful in applications such as augmented reality, robotics, or autonomous navigation, where precise motion tracking is essential. The method ensures that the adjusted motion data accurately reflects the true movement of the tracked object, reducing errors in positioning and orientation calculations.
24. The method of claim 21, wherein the first video source is mounted to a helmet and the second video source is mounted to a weapon.
A system captures and processes video from multiple sources to enhance situational awareness for users, particularly in military or tactical environments. The system includes at least two video sources: one mounted to a helmet worn by a user and another mounted to a weapon. The helmet-mounted camera provides a first-person perspective, capturing the user's field of view, while the weapon-mounted camera offers a targeted view aligned with the weapon's aim. The system synchronizes and combines the video feeds, allowing the user to monitor both perspectives simultaneously. This dual-camera setup improves situational awareness by providing a broader view of the environment while maintaining focus on the weapon's target. The system may further process the video feeds to enhance image quality, stabilize footage, or overlay additional data, such as targeting information or environmental data. The combined video output can be displayed on a heads-up display (HUD) or other output device, enabling real-time decision-making. This approach addresses the limitations of single-camera systems by providing complementary perspectives, reducing blind spots, and improving accuracy in dynamic environments. The system is particularly useful for military personnel, law enforcement, or other users requiring enhanced visual situational awareness.
25. The method of claim 21, wherein the data associated with the portion of the first image is based on pixels for a first region of the first image, and wherein the data associated with the portion of the second image is based on pixels for a second region of the second image.
This invention relates to image processing, specifically methods for analyzing and comparing regions within multiple images. The problem addressed involves accurately correlating or matching specific portions of different images, which is essential for applications like object tracking, image alignment, or change detection. The method involves extracting data from defined regions within each image to enable precise comparison or processing. The technique processes a first image and a second image, where each image contains distinct regions of interest. For the first image, data is derived from pixels within a first region, and for the second image, data is derived from pixels within a second region. These regions may correspond to the same physical area in different images or different parts of the same scene. The extracted data can include pixel values, statistical features, or other image characteristics, allowing for detailed analysis or matching between the regions. This approach improves accuracy in tasks such as image registration, object recognition, or motion estimation by focusing on specific areas rather than the entire image. The method is particularly useful in applications requiring high precision, such as medical imaging, surveillance, or autonomous navigation.
27. The system of claim 26, wherein the first motion data and the third motion data comprise data from an inertial measurement unit (IMU) sensor coupled to the first video source, and wherein the second motion data and the fourth motion data comprise data from an IMU sensor coupled to the second video source.
This invention relates to a system for capturing and processing video data from multiple video sources, particularly focusing on motion data synchronization between the sources. The system addresses the challenge of accurately aligning video streams from different cameras when their motion data is derived from inertial measurement units (IMUs) attached to each camera. The invention ensures that motion data from each IMU is properly correlated with the corresponding video stream, enabling precise synchronization and stabilization of the video outputs. The system includes at least two video sources, each equipped with an IMU sensor that records motion data. The first video source generates first motion data and third motion data, while the second video source generates second motion data and fourth motion data. The motion data from each IMU is used to track the movement of the respective camera, allowing for accurate alignment of the video streams. This synchronization is critical for applications requiring high-fidelity video capture, such as virtual reality, augmented reality, and professional filmmaking, where seamless integration of multiple video feeds is essential. The system enhances the reliability and accuracy of motion tracking, reducing errors that could arise from misalignment between the video and motion data.
28. The system of claim 26, wherein the motion data adjustment comprises an amount of rotation.
A system for adjusting motion data in a tracking or navigation application involves processing sensor inputs to correct positional or orientation errors. The system includes a sensor module that collects motion data from one or more sensors, such as accelerometers, gyroscopes, or magnetometers, and a processing module that analyzes this data to detect deviations from expected movement patterns. The system further includes an adjustment module that modifies the motion data to compensate for these deviations, ensuring accurate tracking or navigation. The adjustment module applies a rotation transformation to the motion data, correcting angular discrepancies caused by sensor drift or external interference. This rotation adjustment may involve rotating the data by a specific angle or applying a series of incremental rotations to align the tracked motion with a reference frame or expected trajectory. The system may also include a calibration module that periodically recalibrates the sensors to maintain accuracy over time. The overall goal is to improve the reliability of motion tracking in applications such as augmented reality, robotics, or wearable devices, where precise movement data is critical.
29. The system of claim 26, wherein the first video source is mounted to a helmet and the second video source is mounted to a weapon.
A system captures and processes video from multiple sources to enhance situational awareness for users, particularly in high-risk environments such as military or law enforcement operations. The system includes at least two video sources: one mounted to a helmet and another mounted to a weapon. The helmet-mounted video source provides a first-person perspective, capturing the user's field of view, while the weapon-mounted video source captures a targeted or weapon-aligned perspective. The system synchronizes and integrates the video feeds, allowing the user or a remote operator to view both perspectives simultaneously or switch between them. This dual-camera setup improves situational awareness by providing complementary visual data, such as the user's surroundings and the weapon's aim, which is critical for navigation, target identification, and threat assessment. The system may also include processing capabilities to overlay additional data, such as targeting information or environmental annotations, onto the video feeds. The integration of helmet and weapon-mounted cameras ensures real-time, context-aware visual feedback, reducing reaction times and improving operational efficiency in dynamic environments.
30. The system of claim 26, wherein the data associated with the portion of the first image is based on pixels for a first region of the first image, and wherein the data associated with the portion of the second image is based on pixels for a second region of the second image.
This invention relates to image processing systems that analyze and compare regions of multiple images. The system addresses the challenge of accurately correlating and processing specific portions of images, particularly when those portions may be misaligned or distorted. The invention focuses on extracting and comparing data from defined regions within two or more images to improve tasks such as object detection, image registration, or feature matching. The system processes a first image and a second image, where each image contains a portion of interest. The data associated with the portion of the first image is derived from pixels within a first region of the first image, while the data associated with the portion of the second image is derived from pixels within a second region of the second image. The regions may be predefined or dynamically determined based on alignment, feature detection, or other criteria. The system may use these extracted data sets to perform comparisons, transformations, or other operations to align, enhance, or analyze the images. This approach allows for precise handling of image regions, enabling applications such as medical imaging, satellite imagery, or augmented reality, where accurate region-based processing is critical. The system ensures that only relevant pixel data from each region is used, improving efficiency and accuracy in image analysis tasks.
32. The non-transitory machine-readable medium of claim 31, wherein the first motion data and the third motion data comprise data from an inertial measurement unit (IMU) sensor coupled to the first video source, and wherein the second motion data and the fourth motion data comprise data from an IMU sensor coupled to the second video source.
This invention relates to video stabilization systems that use motion data from inertial measurement units (IMUs) to correct shaky or unstable video footage. The problem addressed is the difficulty in producing smooth, stabilized video when capturing footage with handheld or moving cameras, where unintended motion can degrade video quality. The system involves at least two video sources, each equipped with an IMU sensor. The first video source captures first video data and generates first motion data from its IMU, while the second video source captures second video data and generates second motion data from its IMU. The system also processes third motion data from the first video source's IMU and fourth motion data from the second video source's IMU. These motion datasets are used to analyze and compensate for unwanted motion, ensuring that the final stabilized video output is smooth and free from jitter. The IMU data provides real-time motion information, including acceleration, rotation, and orientation, which is synchronized with the video frames. By comparing and processing the motion data from both video sources, the system can detect and correct for motion artifacts, such as camera shake or parallax errors, resulting in improved video stabilization. This approach enhances the quality of video captured in dynamic environments, such as action sports, aerial filming, or handheld cinematography.
33. The non-transitory machine-readable medium of claim 31, wherein the motion data adjustment comprises an amount of rotation.
A system for processing motion data from a wearable device, such as a smartwatch or fitness tracker, addresses inaccuracies in motion tracking due to device orientation changes. The system captures raw motion data from sensors, including accelerometers and gyroscopes, and applies adjustments to correct for rotational misalignment. These adjustments involve calculating an angular offset between the device's current orientation and a reference orientation, then applying a corresponding rotation to the motion data. The rotation adjustment ensures that motion measurements accurately reflect the user's movements rather than the device's orientation. This correction is particularly useful for activities like running or cycling, where device positioning may vary. The system may also incorporate additional sensor data, such as magnetometer readings, to refine the rotational adjustment. By dynamically compensating for orientation changes, the system improves the accuracy of motion tracking, enabling more reliable fitness metrics and activity monitoring. The method is implemented via software stored on a non-transitory machine-readable medium, ensuring portability and compatibility with various wearable devices.
34. The non-transitory machine-readable medium of claim 31, wherein the first video source is mounted to a helmet and the second video source is mounted to a weapon.
A system captures and processes video from multiple sources to enhance situational awareness for users, particularly in high-risk environments. The invention addresses the challenge of integrating video feeds from different perspectives to provide a comprehensive view of a user's surroundings. The system includes at least two video sources, where one is mounted to a helmet worn by the user and another is mounted to a weapon held by the user. The helmet-mounted camera captures a first-person perspective, while the weapon-mounted camera provides a targeted view aligned with the weapon's direction. The system synchronizes and processes these video feeds to generate a unified output, which may include overlaying the weapon-mounted camera's view onto the helmet-mounted camera's feed or displaying them side-by-side. This integration allows the user to monitor both their immediate surroundings and the weapon's target simultaneously, improving reaction time and accuracy. The system may also include additional features such as image stabilization, low-light enhancement, and real-time annotations to further assist the user. The invention is particularly useful for military, law enforcement, or other applications where situational awareness is critical.
35. The non-transitory machine-readable medium of claim 31, wherein the data associated with the portion of the first image is based on pixels for a first region of the first image, and wherein the data associated with the portion of the second image is based on pixels for a second region of the second image.
This invention relates to image processing, specifically techniques for analyzing and comparing regions of multiple images. The problem addressed involves accurately correlating data from different images, particularly when the images may have variations in alignment, scale, or content. The solution involves a method for processing image data stored on a non-transitory machine-readable medium, where the data associated with portions of two different images is derived from specific regions within each image. The first image is divided into a first region, and the second image is divided into a second region. The data extracted from these regions is then used for further analysis, such as comparison, alignment, or feature extraction. This approach ensures that the data being processed is spatially consistent, improving the accuracy of subsequent operations. The method may be applied in various applications, including image recognition, object tracking, and medical imaging, where precise region-based analysis is critical. The invention enhances the reliability of image processing by focusing on defined regions rather than the entire image, reducing errors caused by irrelevant or misaligned data.
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April 19, 2022
April 23, 2024
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